mcp-rememberizer-vectordb 0.1.0

A Model Context Protocol server for interacting with Rememberizer Vector Store (https://docs.rememberizer.ai/developer/vector-stores).

Rememberizer Vector Store MCP Server

A Model Context Protocol server for LLMs to interact with Rememberizer Vector Store.

Components

Resources

The server provides access to your Vector Store's documents in Rememberizer.

Tools

1. rememberizer_vectordb_search

   • Search for documents in your Vector Store by semantic similarity
   • Input:
     • q (string): Up to a 400-word sentence to find semantically similar chunks of knowledge
     • n (integer, optional): Number of similar documents to return (default: 5)

2. rememberizer_vectordb_agentic_search

   • Search for documents in your Vector Store by semantic similarity with LLM Agents augmentation
   • Input:
     • query (string): Up to a 400-word sentence to find semantically similar chunks of knowledge. This query can be augmented by our LLM Agents for better results.
     • n_chunks (integer, optional): Number of similar documents to return (default: 5)
     • user_context (string, optional): The additional context for the query. You might need to summarize the conversation up to this point for better context-awared results (default: None)

3. rememberizer_vectordb_list_documents

   • Retrieves a paginated list of all documents
   • Input:
     • page (integer, optional): Page number for pagination, starts at 1 (default: 1)
     • page_size (integer, optional): Number of documents per page, range 1-1000 (default: 100)
   • Returns: List of documents

4. rememberizer_vectordb_information

   • Get information of your Vector Store
   • Input: None required
   • Returns: Vector Store information details

5. rememberizer_vectordb_create_document

   • Create a new document for your Vector Store
   • Input:
     • text (string): The content of the document
     • document_name (integer, optional): A name for the document

6. rememberizer_vectordb_delete_document

   • Delete a document from your Vector Store
   • Input:
     • document_id (integer): The ID of the document you want to delete

7. rememberizer_vectordb_modify_document

   • Change the name of your Vector Store document
   • Input:
     • document_id (integer): The ID of the document you want to modify

Installation

Using uv (recommended)

When using uv, no specific installation is needed. Use uvx to directly run mcp-rememberizer-vectordb.

Configuration

Environment Variables

The following environment variables are required:

• REMEMBERIZER_VECTOR_STORE_API_KEY: Your Rememberizer Vector Store API token

You can register an API key by create your own Vector Store in Rememberizer.

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

"mcpServers": {
  "rememberizer": {
      "command": "uvx",
      "args": ["mcp-rememberizer-vectordb"],
      "env": {
        "REMEMBERIZER_VECTOR_STORE_API_KEY": "your_rememberizer_api_token"
      }
    },
}

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /path/to/directory/mcp-rememberizer-vectordb/src/mcp_rememberizer_vectordb run mcp-rememberizer-vectordb

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.